Use reference documentation tools with AI agents (www.stromcapital.fi)

Recent advancements in AI coding workflows highlight the significance of integrating dynamic reference documentation tools to enhance the accuracy of AI-generated code. Many developers face the challenge of dealing with outdated libraries, leading to issues where AI models produce plausible yet incorrect code snippets based on their training data, which may not reflect the current versions in use. This discrepancy can waste valuable development time as developers sort through misleading outputs. To address this issue, tools like Context7 and GitHits have emerged. Context7 functions as an MCP server that retrieves up-to-date, version-specific documentation dynamically, accommodating more than 1,000 public libraries and allowing integration of private documentation with a paid plan. Conversely, GitHits provides real-time code examples by searching through GitHub repositories. These innovations enhance the AI's reasoning by supplying accurate context, leading to more reliable code outputs with fewer errors. As a result, developers can expect improved productivity and less reliance on manual corrections, making documentation retrieval an essential component of modern AI-assisted development tools.